Some digital image processing applications designed to enhance the appearance of processed digital images take explicit advantage of the noise characteristics associated with the digital images. For example, U.S. Pat. No. 5,923,775 to Snyder et al. discloses a method of digital image processing which includes a step of estimating the noise characteristics of a digital image and using the estimates of the noise characteristics in conjunction with a noise removal system to reduce the amount of noise in the digital image. The method described by Snyder et al. is designed to work for individual digital images and includes a multiple step process for the noise characteristics estimation procedure. First, the residual signal is formed from the digital image obtained by applying an edge detecting spatial filter to the digital image. This first residual signal is analyzed to form a mask signal, which determines what regions of the digital image are more or less likely to contain image structure content. The next step includes forming a second residual signal using a Laplacian spatial filter and masking the second residual signal in image regions unlikely to contain image structure content defined by the mask signal. The noise magnitude for the digital image is determined by calculating the standard deviation of the masked second residual signal as a function of the pixel values. The last step of the procedure is the application of a noise removal algorithm that makes use of the estimated noise standard deviation values. Snyder et al. use the method disclosed in commonly assigned U.S. Pat. No. 5,091,972 to remove the noise from the digital image.
The method disclosed by Snyder et al. effectively uses a subset of pixels of the digital image for the purposes of improving the accuracy of the noise estimation procedure. It is known in the art that statistical approximation methods can achieve sufficiently accurate results by analyzing a subset of data points taken as a representative sampling of the entire set of data points. This can be done without significantly sacrificing the accuracy of results, as long as enough sample data points are used. The difficulty in achieving accurate noise estimation results while using a subset of data points lies in the method of determining which data points and how many data points to use.